Sunday June 28, 2009
2:00 - 3:30 pm - First afternoon
session - Isabelle Guyon chair
- 2:00 - 2:50 - Presentation of the organizers,
Analysis of the Results of the KDD Cup 2009: Fast Scoring on
a Large Marketing Database [Slides PPT][Slides PDF][Paper PDF]
- 2:50 - 3:10 - Winners fast track: IBM research,
USA, Winning the KDD Cup Orange Challenge with Ensemble Selection
[Slides PDF][Paper PDF]
- 3:10 - 3:30 - Winners slow track: University
of Melbourne, Australia, Predicting customer behaviour: The University
of Melbourne's KDD Cup report (via video)
[Slides PDF][Handouts][Paper PDF]
3:30 - 4:00 pm Coffee Break
4:00 - 5:30 pm - Second afternoon
session - David Vogel chair
- 4:00 - 4:15 - Second place fast track: ID
analytics, USA, A Combination of Boosting and Bagging for KDD Cup 2009
- Fast Scoring on a Large Database [Slides PDF][Paper PDF]
- 4:15 - 4:30 - Third place fast track: Peter
Frey and David Slate, USA, Old Dogs with New Tricks [Slides PDF][Abstract PDF]
- 4:30 - 4:45 - Third place slow track: National
Taiwan University, An Ensemble of Three Classifiers for KDD Cup 2009:
Expanded Linear Model, Heterogeneous Boosting, and Selective Naïve
Bayes [Slides PDF][Slides PPT][Paper PDF]
- 4:45 - 5:00 - Miklos Kurucz, Hungarian Academy
of Sciences (6th slow track. Just after 3rd prize winner) KDD Cup
2009 @ Budapest: feature partitioning and boosting [Slides PDF][Paper PDF]
- 5:00 - 5:15 - Small challenge winner: RWTH
Aachen University, Germany, Logistic Model Trees with AUC Split
Criterion for the KDD Cup 2009 Small Challenge [Abstract][Slides PDF][Poster PDF][Paper PDF]
5:15 - 5:45 Discussion -
Isabelle Guyon moderator
6:00 pm - Award Presentations
Monday June 29, 2009
Reception at the Hotel de Ville. The winners of the KDD cup were presented
with a plaque. A poster space was reserved to show the results of the
top ranking participants. Below are some pictures of the reception.
Supplementary Material
- Raphaël Féraud, Marc Boullé, Fabrice Clérot, Françoise Fessant, Vincent Lemaire, The Orange Customer Analysis Platform [Tech Report]
- LatentView Analytics, Ensemble Approach to Predict Churn,
Appetency and Up-Sell [Extended
Abstract PDF][Tech Report]
- Thierry Van de Merckt et al, Vadis Consulting, Belgium, Fast
automatic classification in extremely large data sets: the RANK approach
[Tech Report]
- Balazs Kegl and Robert Busa-Fekete, University of Paris-Sud, France,
Accelerating AdaBoost using UCB [Poster PDF][Paper PDF]
- Vladimir Nikulin and Geoffrey J. McLachlan, University of Queensland,
Australia, Classification of Imbalanced Marketing Data with Balanced
Random Sets [Paper
PDF]
- Daria Sorokina, Carnegie Mellon University, USA, Application
of Additive Groves Ensemble with Multiple Counts Feature Evaluation to
KDD Cup’09 Small Data Set [Paper PDF]
The proceedings of the workshop were published in
JMLR W&CP, vol. 7.
